| Challenge: | a recent study has investigated the nature of vowel inventories, i.e., which vowels a language contains . a probabilistic approach does not rule out linguistic systems completely, but it can position phenomena on a scale from very common to very improbable. |
| Approach: | They propose a generative probability model of vowel inventory typology based on acoustic information rather than discrete symbols from the international phonetic alphabet. |
| Outcome: | The proposed model uses acoustic information rather than discrete symbols from the phonetic alphabet. |
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| Challenge: | a generative model of languages based on principles-and-parameters posits that languages toggle on or off . linguistic typologists use a set of universal parameters to determine which languages toggle . we show that the correlation between parameters is significant, and that it is not enough to write down the set of parameters available to languages. |
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From Phonology to Syntax: Unsupervised Linguistic Typology at Different Levels with Language Embeddings (N18-1)
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| Challenge: | linguistic typology is the classification of languages according to their linguistic properties. |
| Approach: | They learn distributed language representations which can be used to predict typological properties on a massively multilingual scale. |
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On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling (D18-1)
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| Challenge: | a key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. |
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Uncovering Probabilistic Implications in Typological Knowledge Bases (P19-1)
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| Challenge: | linguistic typology is concerned with mapping out the relationships between languages with structural and functional properties. |
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A Corpus for Large-Scale Phonetic Typology (2020.acl-main)
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Elizabeth Salesky, Eleanor Chodroff, Tiago Pimentel, Matthew Wiesner, Ryan Cotterell, Alan W Black, Jason Eisner
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GPT-SW3: An Autoregressive Language Model for the Scandinavian Languages (2024.lrec-main)
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Ariel Ekgren, Amaru Cuba Gyllensten, Felix Stollenwerk, Joey Öhman, Tim Isbister, Evangelia Gogoulou, Fredrik Carlsson, Judit Casademont, Magnus Sahlgren
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On Generative Spoken Language Modeling from Raw Audio (2021.tacl-1)
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Kushal Lakhotia, Eugene Kharitonov, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Benjamin Bolte, Tu-Anh Nguyen, Jade Copet, Alexei Baevski, Abdelrahman Mohamed, Emmanuel Dupoux
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What is ”Typological Diversity” in NLP? (2024.emnlp-main)
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| Challenge: | linguistic typology is commonly used to motivate language selections, but there are no set definitions or criteria for such claims. |
| Approach: | They propose to use linguistic typology to motivate language selections on the basis that a broad typological sample ought to imply generalization across a wide range of languages. |
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How to Compute the Probability of a Word (2024.emnlp-main)
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| Challenge: | Language models estimate a probability distribution over strings in a natural language . many recent linguistic studies have been incorrectly computing word probabilities . |
| Approach: | They propose to use the correct method to compute word probabilities . they highlight issues when relying on models that use end-of-word tokenisers . |
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Can Language Models Learn Typologically Implausible Languages? (2026.tacl-1)
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| Challenge: | Language models provide a naturalistic framework for studying artificial language learning . authors: typological universals and tendencies are thought to be caused by a learning bias . |
| Approach: | They propose to train LMs on highly naturalistic counterfactual versions of English and Japanese . they show that LM learn subtly implausible languages more slowly . |
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